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HOTSPOT You create an Azure Machine Learning dataset containing automobile price data The dataset includes 10,000 rows and 10 columns You use Azure Machine Learning Designer to transform the dataset by using an Execute Python Script component and custom code. The code must combine three columns to create a new column. You need to configure the code function. Which configurations should you use? lo answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
DRAG DROP You create an Azure Machine Learning workspace and an Azure Synapse Analytics workspace with a Spark pool. The workspaces are contained within the same Azure subscription. You must manage the Synapse Spark pool from the Azure Machine Learning workspace. You need to attach the Synapse Spark pool in Azure Machine Learning by usinq the Python SDK v2. Which three actions should you perform in sequence? To answer move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
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HOTSPOT You create an Azure Machine Learning model to include model files and a scorning script. You must deploy the model. The deployment solution must meet the following requirements: * Provide near real-time inferencing. * Enable endpoint and deployment level cost estimates. * Support logging to Azure Log Analytics. You need to configure the deployment solution. What should you configure? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
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you create an Azure Machine learning workspace named workspace1. The workspace contains a Python SOK v2 notebook mat uses Mallow to correct model coaxing men's anal arracks from your local computer. Vou must reuse the notebook to run on Azure Machine I earning compute instance m workspace. You need to comminute to log training and artifacts from your data science code. What should you do?
You deploy a real-time inference service for a trained model. The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates. You need to implement a monitoring solution for the deployed model using minimal administrative effort. What should you do?

Question 35 - DP-100 discussion

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Note: This question-is part of a series of questions that present the same scenario. Each question-in the series contains a unique solution that might meet the stated goals. Some question-sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question-in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You train a classification model by using a logistic regression algorithm.

You must be able to explain the model's predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.

You need to create an explainer that you can use to retrieve the required global and local feature importance values.

Solution: Create a PFIExplainer.

Does the solution meet the goal?

A.
Yes
Answers
A.
Yes
B.
No
Answers
B.
No
Suggested answer: A

Explanation:

Permutation Feature Importance Explainer (PFI): Permutation Feature Importance is a technique used to explain classification and regression models. At a high level, the way it works is by randomly shuffling data one feature at a time for the entire dataset and calculating how much the performance metric of interest changes. The larger the change, the more important that feature is. PFI can explain the overall behavior of any underlying model but does not explain individual predictions.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability

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asked 02/10/2024
Verónica Crespo
36 questions
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